Other Computer Engineering Commons^™

Design And Realization Of Concurrent Cryptosystem For Medical Image Privacy On Reconfigurable Hardware, Vinoth Raj R

Theses and Dissertations

The protection of medical image privacy plays a crucial role in maintaining confidentiality for the secure storage and transmission of patient’s sensitive healthcare data. Medical images are the widely used data type in the e-healthcare sector. Traditional cryptographic algorithms have limitations when applied to large-scale medical image datasets due to their high computational requirements. The primary goal of this research work is to design and implement indigenous algorithms to provide confidentiality for grayscale and color DICOM (Digital Imaging and Communications in Medicine) images through an encryption process. The research leverages the benefits of reconfigurable hardware, namely the Field-Programmable Gate Arrays …

State Of The Grid: Cybersecurity Best Practices For The Utility Industry, Corban Garcia

SACAD: Scholarly Activities

Developing a strong cybersecurity posture is essential for protecting critical infrastructure, especially in the utility industry. This study analyzes a multi-layered cybersecurity approach that integrates risk management, technology, governance, and workforce training. Based on an examination of industry literature, six key steps were identified. These include assessing security posture, developing policies, implementing security controls, employee training, incident response planning, and regular audits. Each step plays a vital role in mitigating cyber threats and ensuring operational resilience. A comparative analysis of cybersecurity frameworks and real-world incidents highlights the necessity of proactive security strategies. Without these foundational steps, organizations face greater risks …

Harmonicthreads – An Interface That Supports Accessibility In Musical Interaction, Ellie Nguyen, Miyuki Weldon, Franceli L. Cibrian

Engineering Faculty Articles and Research

Traditional musical instruments often can create boundaries due to their cost, training, mobility, and cognitive requirements, making musical expression inaccessible. To address this challenge, we developed HarmonicThreads, a novel pervasive computing interface consisting of a responsive, flexible fabric. HarmonicThreads provides a tactile and auditory experience, allowing users to easily create and control sounds. Using embedded sensors and real-time processing, HarmonicThreads interprets the user's natural movements and interactions to create adaptable musical outputs. This enables context-aware musical interaction, demonstrating the potential of pervasive interfaces in reducing barriers and making musical expression more accessible.

Methods For Detecting Anomalies In Network Traffic Based On One-Class Svm Technology, Komil Kerimov, Sardor Kurbanov, Zarina Azizova

Chemical Technology, Control and Management

This article is dedicated to the research and application of the One-Class Support Vector Machines method for detecting anomalies in network traffic. It examines the problems of detecting anomalies in network traffic and proposes a methodology for using One-Class SVM, including an overview of the main concepts and formulas of the algorithm. A discussion of the results of One-Class SVM is presented, including interpretation, advantages, limitations and possible directions for development of the proposed technique, as well as the practical significance of using the proposed method for detecting anomalies in network traffic.

Two-Key Dependent Permutation (Tkdp) And Its Applications In Information Security, Arulmani K

Theses and Dissertations

Two-Key Dependent Permutation (TKDP) algorithm for generating permutation sequences of fixed sizes, TKDP based Symmetric Block Cipher (TKDPSBC) and TKDP Audio encryption are being proposed in this thesis. TKDP algorithm is capable of generating different sequences for different key pairs. This makes it suitable for constructing dynamic S-boxes and P-boxes that have more degree of randomness and non-linearity to resist cryptanalytic attacks. Rigorous statistical tests validate the efficacy of the generated permutation sequences, affirming their suitability for cryptographic applications in conjunction with Fiestel network-based block ciphers. TKDPSBC encrypts a plaintext block into a ciphertext block of the same size. TKDP …

Machine Learning And Shap Interpretability For Chronic Disease Understanding, Nnaemeka Charles Igwe, Khandaker Mamun Ahmed

SDSU Data Science Symposium

Non-communicable diseases (NCDs), such as diabetes, are major global health concerns influenced by various health parameters and lifestyle choices. Traditional methods struggle to efficiently predict and manage these conditions due to the complexity and diversity of medical data. There is a need to leverage machine learning algorithms and modern computational tools to accurately predict diabetes, improve diagnosis, and provide actionable insights for better healthcare outcomes. In this project we study the application of machine learning methods for predicting NCDs such as diabetes. Moreover, we leverage hyperparameter tuning techniques for model development and SHapley Additive exPlanation (SHAP) for results interpretations and …

Correlations Between Song Popularity And Their Audio Features Using Machine Learning, Rong Chen

Dissertations, Theses, and Capstone Projects

This project is an interactive visual project that explores the relationship between audio features and song popularity on Spotify using machine learning techniques. Through the collection of nearly half a million songs and implementation of seven different machine learning models, including Linear Regression, Random Forest, Decision Trees, and Gradient Boosting, I investigated how audio characteristics correlate with a song's popularity ranking. The project utilized MongoDB for data storage, Spotipy for API integration, and Streamlit with Plotly for visualization. This work provides insights into the practical challenges of large-scale music analysis and the relationship between technical audio characteristics and commercial success, …

Assessment Of Risk Factor Prediction Using Machine Learning Techniques And Hybrid Approach Based On Soft Sets, Menaga A

Theses and Dissertations

Cardiovascular diseases (CVDs) are the leading cause of mortality worldwide, and India reports a significantly high death rate due to its large population base and the increasing prevalence of non-communicable diseases. National statistics indicate that 20–27% of deaths in India are attributed to CVDs, with the proportion steadily rising over the years. Recognizing the urgency of early detection and risk prevention, the World Health Organization (WHO) introduced “The Global Action Plan for the Prevention and Control of Non-Communicable Diseases (2013–2020),” emphasizing early identification, risk reduction, and timely treatment. In this context, decision-making applications have gained importance across domains especially healthcare …

Cyber Crimes And Mechanisms To Confront Them - The United Arab Emirates As À Model, Aicha Kada Benabdallah, Mohammed Samir Ayad

Journal of Police and Legal Sciences

The technological factor is a double-edged sword; It is a factor of strength for the state as a result of the development it achieves through exploiting modern technologies and information system, and a factor of weakness for it through exploiting modern technology against it to achieve special interests aimed at destabilizing the security and stability of states.

This research paper attempts to shed light on cybercrimes' various forms and characteristics. Today's crimes are different from yesterday's crimes. Considering that it is rapidly spreading and more complex; This is what puts countries in constant search for ways out and …

Feasibility And Acceptability Of The Mazi Umntanakho Digital Tool In South African Settings: A Qualitative Evaluation, Catherine E. Draper, Caylee J. Cook, Elizabeth A. Ankrah, Jesus A. Beltran, Franceli L. Cibrian, Kimberley D. Lakes, Hanna Mofid, Lucretia Williams, Gillian R. Hayes

Engineering Faculty Articles and Research

To address the need for interventions targeting social emotional development and mental health of young children in South Africa, the Mazi Umntanakho (‘know your child’) digital tool was co-designed, and piloted with caregivers and 3–5-year-old children involved in home visiting programmes promoting early childhood development. The aim of this study was to qualitatively evaluate the feasibility and acceptability of this tool in four urban and four rural low-income communities, from the perspective of home visitors and caregivers. Focus groups were conducted with home visitors (n = 117) and caregivers (n = 72). Issues relating to the feasibility of …

Indoor Localization With Ensemble Machine Learning Via Visible Light Communication Channels, Alzahraa M. Ghonim, Wessam M. Salama

Journal of Engineering Research

An indoor localization system based on received signal strength, visible light communication (VLC) and several machine learning approaches is proposed in this paper. Our proposed framework is divided into two strategies. The first one is consisting of gathering our dataset based on MATLAB software to create indoor VLC channel model. While the second phase is depending on training the gained dataset using ensemble machine learning models. Specifically, random forest, decision tree and gradient boosting models. In order to evaluate the robustness of the proposed framework, several evaluation metrics are applied, specifically, training time, testing time, classification accuracy (CA), area under …

Sentiment Analysis For Stock Market Prediction Using Machine Learning Techniques, Rajendiran P

Theses and Dissertations

Sentiment analysis has become one of the most important procedures to predict the stock market behaviour according to the customer reviews about a particular topic such as news, movie, event, and remarks related to the product. Due to the huge number of reviews generated from the customer, for analyzing information in an accurate manner. In order to detect general view of product, sentiment analysis technique is performed. Lately, the majority of research works is designed for Sentiment analysis by application of an organization and ranking techniques. But it suffers less exactness of the accurate classification of the customer reviews.

The …

Interfaces Gráficas Y Género: Impacto En Discursos Normativos, Marco V. Ferruzca, Paulo C. Portilla, Juan Villegas, Román A. Mora

GDI. Revista de investigación de Género, Diseño e Innovación

El diseño de interfaces gráficas de usuario generalmente considera una reflexión sobre propiedades vinculadas a aspectos visuales como el color, las imágenes, la tipografía, etc. Asimismo, algunos aspectos del usuario se ponen también a consideración como su edad, cultura, experiencia, género, entre otros. Sin embargo, la noción de género sólo se reduce a un binarismo para definir si la interfaz gráfica encaja en la categoría mujer u hombre. Muy pocos investigadores se han detenido a profundizar en el discurso heteronormativo que puede desprenderse de la interpretación de significado de una interfaz gráfica como consecuencia de la interrelación entre usuariogénerocomputadora. El …

Event-Based Histogram Of Gradients For Lane Detection, Ganesh Gupta

Computer Science and Engineering Theses - Archive

In the rapidly evolving landscape of autonomous driving technology, lane detection systems stand as fundamental guardians of vehicular safety. The National Highway Traffic Safety Administration identifies unintentional lane departures as responsible for approximately one-third of all road accidents—a sobering statistic that underscores the critical importance of robust lane detection methodologies. This thesis embarks on an academic exploration at the intersection of neuromorphic engineering and computer vision, examining how the distinctive properties of event-based cameras might be harnessed to enhance lane detection capabilities under challenging environmental conditions. Unlike conventional frame-based imaging sensors that capture entire scenes at fixed intervals, event-based cameras …

Exploring Instruction Generation For Uavs: Dataset Adaptation, Model Behavior, And Diagnostic Insights, Seyedarman Vaziri Bozorg

Computer Science and Engineering Theses - Archive

This thesis explores the development of an answering agent capable of generating natural language instructions for unmanned aerial vehicles (UAVs), grounded in a limited, real-world dialogue dataset. The objective is to adapt a static dataset into a training pipeline that can support instruction generation and serve as a foundation for future interactive systems involving question-asking agents and internal dialogue. A hybrid architecture is implemented using a semantic teacher model (MPNet) and a T5-base encoder-decoder trained with contrastive and supervised objectives. The adapted training process yields statistically acceptable performance across standard evaluation metrics. However, qualitative analysis reveals a mismatch between metric …

Training Data Privacy In Machine Learning: A Systematization Of Attacks And Defenses, Mohammad Sufyaan Saeed

Computer Science and Engineering Theses - Archive

Training and deploying Machine Learning (ML) models introduce significant data confidentiality risks, as modern models can inadvertently memorize and leak information about their training data. While attacks such as membership inference and model inversion are well studied, the literature remains fragmented, with inconsistent threat models and unclear relationships across attack classes and defenses. This work presents a Systematization of Knowledge (SoK) that unifies the landscape of training-data privacy attacks and defenses, aligning them with the NIST Adversarial Machine Learning (AML) taxonomy to enable standardized threat modeling and comparison. Our analysis shows that, despite significant progress in characterizing attack vectors, defenses …

Transformer And Recurrent Architectures For Dynamics Prediction And Policy Learning On Long-Horizon Tasks, Vinal Jitendrabhai Gadhiya

Computer Science and Engineering Theses - Archive

Model-based reinforcement learning promises improved sample efficiency by learning environment dynamics and using them for planning or policy improvement. However, the choice of neural architecture for dynamics prediction significantly impacts the model's ability to capture temporal dependencies and maintain long-term context, capabilities crucial for complex, open-world environments.

This thesis investigates three neural architectures for learning world models: Transformer-based, GRU-based, and a hybrid Transformer+GRU approach. We evaluate these architectures on Crafter, a 2D open-world survival environment that requires long-horizon planning and sequential task completion. In Crafter, agents must perform hierarchical sequences of actions, such as collecting wood, placing a table, and …

Multi-Modal Model-Based Optical Flow Estimation For Event-Based Vision, Pritam Karmokar

Computer Science and Engineering Dissertations - Archive

Event cameras offer a fundamentally different sensing paradigm by asynchronously capturing brightness changes at high temporal resolution, directly encoding motion in the scene. However, their sparse and non-traditional data format poses significant challenges for dense motion estimation, particularly in the context of optical flow. Contrast Maximization (CM) has emerged as a powerful model-based framework for estimating optical flow from event data by optimizing the sharpness of motion-compensated event representations. This dissertation builds upon and significantly advances the CM framework through two complementary contributions.

First, we propose Edge-Informed Contrast Maximization (EINCM), a hybrid approach that augments the traditional events-only CM framework …

Deep Neural Network Models For Heatsink Performance Prediction And Optimization In Single Phase Immersion Cooling: Framework For Future Design Tools And Digital Twin Integration, Braxton J. Smith

Mechanical and Aerospace Engineering Theses - Archive

The rapidly rising computational power of modern computing components combined with the advanced packaging techniques being implemented has resulted in exponentially increasing thermal design powers (TDP) from CPUs and GPUs. Traditional air-cooling methods are approaching their effective cooling limits for many of these components, requiring lower supply air temperatures, higher supply air flowrates, and much larger heatsinks to remain feasible. Transitioning from air-cooling to single-phase immersion cooling offers numerous benefits in thermal performance, data-center size reduction, and energy efficiency. To leverage the merits of immersion cooling, the performance of a given heatsink must be predicted and optimized for best performance …

Medical Ai: Solving Healthcare Challenges And Inspiring Ai Innovation, Xiaowei Yu

Computer Science and Engineering Dissertations - Archive

Artificial Intelligence (AI) is transforming healthcare by enabling large-scale analysis of medical data and integrating multimodal information for more comprehensive diagnostics. I present my work addressing fundamental and challenging problems in developing state-of-the-art AI models for medical data analysis, including multimodal brain data and other medical datasets. Additionally, I design brain-inspired AI models by integrating insights from organizational principles of brain networks. Specifically, my research tackles three critical aspects: (1) AI in Computational Neuroscience, where I design deep learning models for brain network analysis to uncover the organizational principles of brain networks; (2) Brain-Inspired AI, where I integrate superior brain …

Immersive Executive Functions Assessment System (Iexec): Integrating Embodied Cognition And Virtual Reality, Hamza Reza Pavel

Computer Science and Engineering Dissertations - Archive

Executive functions (EFs) are higher-order cognitive processes that include working memory, inhibitory control, and cognitive flexibility. These higher-order processes facilitate the achievement of goal-directed behavior and enable both adaptive decision-making and emotional regulation. Traditional EF assessment tools depend on static pen-and-paper tasks or basic computer-based tasks, which fail to capture real-world cognitive complexity and dynamics. Some of these assessment tools are specifically geared towards children or older adults, while others are more generic and designed to be used for people of all ages. This dissertation addresses these limitations by introducing iExec: The Immersive Executive Functions Assessment System, which functions as …

Enabling Energy And Water Sustainability Through Out-Of-Band Emi Sensing And Infrastructure Modeling, Pranjol Sen Gupta

Computer Science and Engineering Dissertations - Archive

As demand for Internet and cloud services surges, data centers have emerged as critical infrastructure—but they are also among theworld’s most energy- andwater-intensive facilities. Effective power management, particularly at the server level, is essential for improving efficiency, reliability, and sustainability. However, server-level power monitoring remains uncommon due to the high cost of hardware instrumentation and the intrusiveness of software-based solutions, especially in shared colocation environments. My research introduces a novel, low-cost, and non-intrusive method for server-level power monitoring using conducted electromagnetic interference (EMI). By analyzing EMI signals captured from higher levels in the power distribution network, this approach estimates individual …

Introducing Catalizer: A Framework For Prototyping Models Of Biological Systems, Andy M. Day

Honors Theses

Modeling the light response system of Nannochloropsis oceanica brings

a set of challenges that make modeling difficult. Notably, potential models

may contain a large number of chemical species. A large number of

species creates a quadratic explosion in the number of potential pathways.

In addition, mathematically defining these pathways is error prone, yet

follows a surprising simple set of rules. We seek to create a domain specific

language which can precisely define these chemical reaction networks.

Once the networks have been defined, they can be exported as procedures

defined in popular programming languages for further analysis.

Social Engineering Scenario Generation For Awareness-Based Attack Resilience, Jade Webb

Master's Projects

Social engineering is found in a strong majority of cyberattacks today, as it is a powerful manipulation tactic that does not require the technical skills of hacking. Calculated social engineers utilize simple communication to deceive and exploit their victims, all by capitalizing on the vulnerabilities of human nature: trust and fear. When successful, this inconspicuous technique can lead to millions of dollars in losses. Social engineering is not a one-dimensional technique; criminals often leverage a combination of strategies to craft a robust yet subtle attack. In addition, offenders are continually evolving their methods in efforts to surpass preventive measures. A …

Cca Analysis Using Computer Vision Techniques, Rahul Thakur

Master's Projects

Coral reefs are an essential part of the marine ecosystem. They perform a wide variety of tasks, some directly and others indirectly. They can produce oxygen, absorb carbon dioxide, along with supporting ocean habitat. Crustose Coralline Algae (“CCA”) plays an important role in helping provide structural support to Coral Reef ecosystems. However, global warming is causing ocean water to become more acidic resulting in coral bleaching. This is leading to changes in coral environments and causing coral deaths at alarming rates. Object detection using computer vision techniques, specifically deep learning, can help to monitor coral reef health and identify CCA …

Retrieval-Augmented Generation (Rag) Chatbots: A Comparative Study Of Claude, Gpt-4o, Deepseek, And Llama, Kalindi Vijesh Parekh

Master's Projects

The use of Retrieval-augmented generation (RAG) in chatbot platforms has transformed academic spaces by significantly improving information accessibility. RAG has become a viable approach to upgrading Large Language Models (LLMs) with external knowledge access in real time. With the growing availability of advanced LLMs such as GPT, DeepSeek, Claude, Gemini, and Llama, there is a growing need to compare RAG systems based on different LLMs. This study compares the responses of four different RAG chatbots using popular LLMs against a uniquely designed evaluation dataset. Specifically, the study compares the responses and performance of closed-source (GPT-4o and Claude) and open-source models …

Smart: Semantic Mapping And Analysis For Regional Terrain Using Multi-Scale U-Net And Topsis, Rashmi Sonth

Master's Projects

Accurate land use classification is the backbone for urban planning. But with poor quality satellite images, varied landscapes and structures which are changing faster than ever, it becomes a challenge to define clear boundaries and hence to urban planning. This research explores the application of deep-learning model for land use classification and asses the suitability of the land. The proposed model combines a multi-scale U-Net architecture with Transformer blocks applied on a multi-spectral satellite images that improves the semantic segmentation greatly across the urban and rural regions. Additionally, a patch-wise segmentation is applied to overcome the common problem of feature …

Detecting Ai-Generated News Articles Using Unsupervised Machine Learning Algorithms, Lilou Sicard-Noel

Master's Projects

The widespread adoption of Large Language Models (LLMs) has revolutionized text generation and heightened concerns over misinformation and the erosion of journalistic integrity. Detecting AI-generated text is critical to addressing these challenges, yet current detection methods face adaptability, scalability, and accuracy limitations. This research paper uses machine-learning techniques to explore the classification of human and AI-generated articles, including a mix of human and AI-written content. The primary focus is on evaluating the effectiveness of clustering algorithms (K-Means and Agglomerative Clustering), auto-encoders, and Part-Of- Speech Tag Transition Matrix Log-Likelihood for distinguishing between AI-generated and human-written texts. Our findings reveal that while …

Mycelia: Cross-Chain Data Oracle Using Frost Signatures, Bala Komatireddy

Master's Projects

The interoperability of heterogeneous blockchain networks is the basis for the widespread application of blockchains in various fields. Cross-chain data oracles play a significant role in enabling distributed applications to exchange data and assets across different blockchains, thereby greatly enriching and expanding the application scenarios and use of blockchains. With the continuous advancement of blockchain technology, more and more researchers and industry participants have begun to focus on developing cross-chain data oracles. Current cross-chain data oracles face issues with trust, as they rely on centralized intermediaries or limited validator networks, increasing the risk of manipulation or single points of failure. …

Retrieval-Augmented Generation For Survival Analysis In Cancers: Methods And Evaluation On The Surveillance, Epidemiology, And End Results Database, Jyothi Vaidyanathan

Master's Projects

Healthcare is one of the most important fields that benefits from advancements in Artificial Intelligence (AI). From classic models like linear regression to cuttingedge transformers, AI is applied across various healthcare subdomains, such as drug discovery, predictive analytics, and personalized medicine, to name a few. These techniques enable medical practitioners to make more informed decisions, significantly improving both the speed and accuracy of diagnoses and treatments. Machine learning has played a transformative role in oncology, especially in areas like early detection, diagnosis, treatment planning, and patient monitoring, by analyzing medical images, clinical information, genomic data, sensor information. Our research aims …